This vignette summarises the findings from the 100 days and 100 lines of code workshop, hosted in December 2022 by Epiverse-TRACE.

What should the first 100 lines of code written during an epidemic look like?

To answer this question, we invited 40 experts, including academics, field epidemiologists, and software engineers, to take part in a 3-day workshop, where they discussed the current challenges, and potential solutions, in data analytic pipelines used to analyse epidemic data.

What R packages and tools are available to use during an epidemic?

To investigate this in a similar setting to what an outbreak response team would experience, workshop participants were divided into groups, and asked to develop a plausible epidemic scenario, that included:

  • A situation report, describing the characteristics of the epidemic

  • A linelist of cases and contact tracing data, by modifying provided datasets containing simulated data

  • A set of questions to address during the analytic process

Groups then exchanged epidemic scenarios and analysed the provided data to answer the questions indicated the previous group, as if they were a response team working to solve an outbreak. Details about each of these outbreak scenarios and the analytic pipelines developed by the groups are summarised in this vignette

Simulating epidemic data

Before the workshop, a fictitious dataset was created, which consisted of a linelist and contact tracing information.

To generate linelist data, the package bpmodels was used to generate a branching process network. Cases were then transformed from the model output to a linelist format. To add plausible hospitalisations and deaths, delay distributions for SARS-CoV were extracted from epiparameter.

To create the contact tracing database, a random number of contacts was generated for each of the cases included in the linelist. These contacts were then assigned a category of became case, under follow up or lost to follow up, at random.

  • Through this workshop, we identified the need for a tool to simulate outbreak data in a linelist format, to test analysis methods and other packages while having control over the characteristics of the test data. For this purpose, an R package is currently in progress, see simulist.

Scenario 1: Novel respiratory disease in The Gambia



Analytic pipeline for scenario 1 (analysed by group 2)


Data analysis step Challenges
Data cleaning Not knowing what packages are available for this purpose
Delay distributions Dealing with right censoring
Accounting for multiple infectors
Population demographics Lacking tools that provide information about population by age, gender, etc.
Risk factors of infection Distinguishing between risk factors vs detecting differences in reporting frequencies among groups
Severity of disease Knowing the prevalence of disease (denominator)
Right censoring
Varying severity of diffeent strains
Contact matching Missing data
Misspellings
Epicurve and maps NA dates entries not included
Reporting levels varying over time
Offspring distribution Right censoring
Time varying reporting efforts
Assumption of a single homogeneous epidemic
Importation of cases
Forecasting Underlying assumption of a given R distribution, e.g., single trend, homogeneous mixing, no saturation


Scenario 2: Outbreak of an unidentified disease in rural Colombia



Analytic pipeline for scenario 2 (analysed by group 3)

  • Data cleaning: manually, using R (no packages specified), to
    • Fix data entry issues in columns onset_date and gender
    • Check for missing data
    • Check sequence of dates: symptom onset → hospitalisation → death
  • Data anonymisation to share with partners
    • fastlink for probabilistic matching between cases ↔︎ contacts, based on names, dates, and ages
  • Case demographics
    • apyramid to stratify data by age, gender, and health status
  • Reproductive number calculation, by using two approaches:
    • Manually, by calculating the number of cases generated by each source case, data management through dplyr and data.table
    • Using serial interval of disease, through EpiEstim or EpiNow2
  • Severity of disease
    • Manual calculation of CFR and hospitalisation ratio
  • Projection of hospital bed requirements
    • EpiNow2 to calculate average hospitalisation duration and forecasting
  • Zoonotic transmission of disease
    • Manual inspection of cases’ occupation
    • Use of IQtree and ggtree to plot phylogenetic data
  • Superspreading
  • Calculation of attack rate
    • Unable to calculate, given the lack of seroprevalence data


Data analysis step Challenges
Data anonymisation Dealing with typos and missing data when generating random unique identifiers
Reproduction number Right censoring
Underestimation of cases due to reporting delays
Projection of hospital bed requirements Incomplete data (missing discharge date)
Undocumented functionality in R packages used
Zoonotic transmission Poor documentation
Unavailability of packages in R
Differentiation between zoonotic transmission and risk factors- need for population data
Attack rate Not enough information provided

Scenario 3: Reston Ebolavirus in the Philippines



Analytic pipeline for scenario 3 (analysed by group 4)


Data analysis step Challenges
Detection of outliers No known tools to use
Severity of disease Censoring
Spillover events Missing data


Scenario 4: Emerging avian influenza in Cambodia



Analytic pipeline for scenario 4 (analysed by group 5)

  • Data cleaning
    • readxl to import data
    • dplyr to remove names
    • Manually scanning through excel to check for errors
  • Reproduction number
  • Severity of disease
    • Manually using R to detect missing cases
    • epiR to check for data censoring


Data analysis step Challenges
Data cleaning No available R packages specific for epidemic data
Reproduction number Difficulty finding parameter estimations in the literature
Severity Missing cases
Need for an R package for systematic censoring analysis


Scenario 5: Outbreak of respiratory disease in Canada



Analytic pipeline for scenario 5 (analysed by group 1)


Data analysis step Challenges
Project structure Working simultaneously on the same script and managing parallel tasks
Data cleaning Losing too many data entries when removing NA rows
Non standardised data
Delay distributions Need to identify the best method to calculate, or compare functionality of tools
Severity of disease Censoring and truncation
Underestimation of mild cases
Zoonotic transmisison Need for specific packages with clear documentation